Qbeast gets $7.6M in funding to streamline data lakehouses queries with multidimensional indexing

Data optimization startup Qbeast Analytics Inc. says it’s going to assist organizations eliminate the “hidden tax” of huge data lakehouse frameworks like Delta Lake after raising $7.6 million in seed funding today.

The round was led by Peak XV’s Surge, formerly often called Sequoia Capital India. Other investors were HWK Tech Investment and Elaia Partners. The cash might be used to fund latest hires and expand its platform to latest use cases, the corporate said.

In accordance with Qbeat, open-source lakehouse architectures similar to Delta Lake, Apache Iceberg and Apache Hudi have grow to be extremely popular within the enterprise today, helping organizations to maintain a lid on exploding data volumes that strain traditional infrastructures. Nevertheless, although these platforms are incredibly powerful and highly scalable, Qbeast points to an infinite “tax” on using them, with as much as 90% of compute resources being wasted on scanning irrelevant data stored inside them.

The issue is that though data lakes are massive, they’re not smart in regards to the way they organize and search through that information. Consequently, queries have gotten each painfully slow and really expensive. “There may be an undesirable compute cost hidden in the info layout that has been highly neglected by the marketplace for data lakehouses,” said Chief Technology Officer Flavio Junequeira, who previously helped create Apache BookKeeper and Apache ZooKeeper.

Qbeast, which was born as a research project on the Barcelona Supercomputing Center, has developed the answer – a data optimization platform that plugs directly into existing Delta, Iceberg and Hudi tables. It features multidimensional indexing capabilities with complex filters across columns similar to time, region and customer segment, enabling customers to look through and query only the info they need to have a look at.

The platform may optimize for either historical or real-time queries in a single table, with simultaneous filtering across any mix of information attributes, in contrast to traditional partitioning tools that only work in single dimensions. What’s more, Qbeast’s drop-in indexing works perfectly with compute engines similar to Databricks, DuckDB, Polars, Snowflake and Spark, so there’s no have to rewrite data pipelines or mess with the underlying storage layer. By making data more efficient to question, Qbeast says, it could possibly increase query speeds by as much as six times, depending on the dataset, while reducing compute costs by as much as 70%.

These are some impressive claims, and that’s precisely what convinced former Microsoft Azure and Amazon Web Services cloud infrastructure veteran Srikanth Satya to simply accept the role of the corporate’s latest chief executive.

Satya, whose appointment was also announced today, said data teams shouldn’t be forced to choose from speed, cost or openness. “We built Qbeast to make high-performance analytics easy and accessible, without locking organizations into proprietary systems,” he explained. “In a world where data is growing faster than ever, we’re here to make sure every company can turn that data into value on their very own terms.”

One other advantage of Qbeast is that its platform plays natively with existing data tools, being compatible with all of the preferred open data formats, so there’s no need for patrons to make any changes to their data infrastructure in any respect. They’ll just drop in its multi-indexing tool and leave the whole lot else because it is.

“We imagine every organization, not only the tech elite, should have the option to extract value from their data without incurring massive cloud costs or hiring a team of engineers to tune performance,” Satya added.

Though Qbeast’s basic platform is up and running and available now, it has an in depth roadmap worked out that may see it add latest capabilities including auto-tuning, adaptive indexing and deeper data engine support, across cloud infrastructure providers and industry verticals. Ultimately, it desires to grow to be the default indexing layer for each open lakehouse architecture, paving the way in which for a future where data innovation isn’t hindered by spiraling compute costs or performance constraints.

HWK TechInvestment CEO and Managing Partner Juan Santamaría said Qbeast is solving a vital and fundamental problem. “Its multi-dimensional indexing layer has the potential to grow to be critical for each company moving to a lakehouse model,” he said.

Images: Qbeast Analytics